title: Improving EMG based classification of basic hand movements using EMD.
abstract: This paper presents a pattern recognition approach for the identification of basic hand movements using surface electromyographic (EMG) data. The EMG signal is decomposed using Empirical Mode Decomposition (EMD) into Intrinsic Mode Functions (IMFs) and subsequently a feature extraction stage takes place. Various combinations of feature subsets are tested using a simple linear classifier for the detection task. Our results suggest that the use of EMD can increase the discrimination ability of the conventional feature sets extracted from the raw EMG signal.
authors.forename: Christos
authors.initials: C
authors.lastname: Sapsanis
authors.forename: George
authors.initials: G
authors.lastname: Georgoulas
authors.forename: Anthony
authors.initials: A
authors.lastname: Tzes
authors.forename: Dimitrios
authors.initials: D
authors.lastname: Lymberopoulos
mesh_terms: D000465:Algorithms; D004576:Electromyography / Q000295:instrumentation / Q000379:methods*; D005260:Female; D006225:Hand / Q000502:physiology*; D006801:Humans; D008297:Male; D009068:Movement; D010363:Pattern Recognition, Automated; D012371:Robotics; D055815:Young Adult